AI Needs Context. Why Flink is Made for Context Engineering

We have mastered Prompt Engineering, but production AI needs Context Engineering: the architectural discipline of curating the information an LLM sees before it answers. The right context window transforms a generic chatbot into a specialized expert.

A perfect prompt is useless if the model is fed stale data or overwhelmed by irrelevant noise. This not only confuses the model but also increases the cost of every query. To build reliable AI Agents, we need a data processing layer that doesn't just move data but actively "engineers" it into a state-ready format for inference. This is where Apache Flink’s new ProcessTableFunctions (PTFs) change the game.

PTFs leverage Flink’s full capabilities, allowing for custom, stateful processing logic within a structured framework.

In this session, we will explore why this kind of stream processing is the natural backbone for Context Engineering and how Flink’s PTFs provide the missing primitives for dynamic context construction. We will demonstrate how to:

Orchestrate context from multiple sources into a single, unified view.

Dynamically prune and format conversation history into a highly compressed state.

Create time-based context windows that are ranked and pre-aggregated in real-time.

Engineer a system that "remembers" users over weeks or months, so conversations pick up exactly where they left off.